Introduction to data visualization

Undergraduate course

Course description

Objectives and Content


The course aims to introduce the students to the basics of data visualization theory, design and


Our personal and working lives are immersed in data - from step counts, mobile weather predictions, to spreadsheets and other data sources that drive decisions on personal, local, and global levels. Data visualization is a proven means to engage with, explore, and contextualize large volumes of information critical to decision making. However, many questions must be answered to create a visualization, such as: What color should I use for this variable? or should I use a bar- or line-chart to find something? Poor decisions may produce visualizations that mislead, hide, or overstate important features of the data, while good decisions result in clear, more truthful depictions of the data. In this course, we will introduce the basics of data visualization design and development. Knowledge of these basics will enable you to produce visual representations of data that are both truthful and effective, and to identify visuals that are misleading about the underlying data. Through a blend of lectures, discussions, and hands-on activities in Python that are friendly to beginners, you will learn about different data types, different user tasks, and be introduced to a "grammar" of visualization to help you to successfully compose, decompose, and evaluate visualizations of data. You will also learn how to effectively incorporate movement and interactivity to better explore and engage with your data

Learning Outcomes

On completion of the course the student should have the following learning outcomes defined in terms of

knowledge, skills and general competence:


The student

  • Has an understanding of the basic principles of visualization theory and techniques across different major data domains
  • Has knowledge of human graphical perception and its relations to visualization design
  • Has an overview of methods to visually encode and interact with data


The student

  • Has gained practical experience in building basic visualizations of data for analysis and communication
  • Is able to critically evaluate and discuss visualizations

General competence

The student

  • recognizes and can critically discuss the role and importance of visualization in contemporary society
  • can explain "visualization literacy"

ECTS Credits

2,5 ECTS

Level of Study


Semester of Instruction

Irregular spring/autumn

Place of Instruction

Required Previous Knowledge
Recommended Previous Knowledge

It may be beneficial to have taken DIGI110 (Fantastic Data) and DIGI111 (Algorithms and Programming)


Credit Reduction due to Course Overlap

INF252: 2,5 studiepoeng

INF253: 2,5 studiepoeng

Access to the Course
For admission to the course, a right to study at the University of Bergen is required.
Teaching and learning methods
3 hours duration per lecture. 1 lectures per week for 4 weeks.
Compulsory Assignments and Attendance

For each topic there will be an obligatory quiz. An obligatory assignment on the core themes. Obligatory quizzes

and activities are only valid during the teaching semester.

Obligatory activities must be completed in the same semester that the course will be taught.

Forms of Assessment
The subject is passed when all obligatory assignments are completed and approved by the instructor.
Grading Scale
Pass/ fail
Reading List

The reading list will be available within July 1st for the autumn semester and December 1st for the spring


Course Evaluation

The course will be evaluated by the students in accordance with the quality assurance system at UiB and the


Examination Support Material
Programme Committee

The Programme Committee is responsible for the content, structure and quality of the study programme and